Smart Irrigation System using IOT Device and Machine Learning

Abstract

Agriculture utilizes a considerable quantity of freshwater resources, and conventional irrigation techniques frequently lead to water being wasted because of poor scheduling and insufficient environmental oversight. This document introduces a Smart Irrigation System that combines Internet of Things (IoT) technology and Machine Learning (ML) to enhance irrigation efficiency and promote sustainable farming practices. The system consistently observes factors such as soil moisture, temperature, and humidity through the use of sensors [6]. A Linear Regression model is utilized to forecast soil moisture levels, whereas a Random Forest model suggests appropriate crops based on the conditions of the soil and climate. By using real-time information and predictive analysis, the system decreases water waste, reduces the need for manual work, and improves agricultural productivity.

Introduction

Conventional irrigation methods rely on fixed schedules or manual observation, often ignoring real-time environmental factors like soil moisture, temperature, and humidity. This leads to overwatering, underwatering, soil damage, and reduced crop yield. Additionally, farmers often lack tools to predict irrigation needs or select suitable crops.

To address these issues, the study proposes a smart irrigation system combining IoT, Machine Learning, and cloud computing. IoT sensors continuously monitor field conditions such as soil moisture, temperature, humidity, and water tank levels. This data is stored and processed in the cloud, enabling remote access and scalability.

Machine learning plays a key role in decision-making:

• A Linear Regression model predicts future soil moisture to enable proactive irrigation.
• A Random Forest model recommends suitable crops based on soil and climate conditions.

The system also includes a user interface that allows farmers to monitor real-time data, irrigation status, moisture predictions, and crop recommendations through mobile or web applications.

The literature review shows that existing systems focus on either automated irrigation or crop recommendation individually, but lack integration. The proposed system addresses this gap by combining prediction, automation, and decision support into a single platform.

Conclusion

The survey is centered around the Smart Irrigation System. The Smart Irrigation System relies on Internet and IoT technologies to assess the environmental situation [1]. It consists of two interacting components. The Smart Irrigation System attempts to address three issues that farmers experience. They include the excessive waste of water due to the inability to estimate the moment when watering should occur, the use of devices which react to certain events instead of preventing them from happening, and the absence of a recommendation system to assist the farmer in choosing the appropriate crop [2], [6]. From the literature review, it is evident that much effort has been dedicated to the use of Internet-based automation of irrigation practices, IoT-based soil moisture forecasting, recommendation systems of crop selection, and cloud-based farming monitoring [1], [4], [6], [9]. However, these efforts usually remain independent from each other. The Smart Irrigation System seeks to integrate these ideas into one system. One component is responsible for predicting soil moisture content to schedule irrigation activities in advance [9]. Another component is focused on providing recommendations regarding the crops which should be used, taking into consideration soil type and weather conditions [4]. Our hypothesis is that the Smart Irrigation System would facilitate conservation of water, growth of crops, and reduced labor intensity [2]. It would also ensure sustainable use of the land for agricultural purposes [3]. Since it is cloud-based, its use would be flexible, and farmers could use it in any place of their convenience [6]. The smart irrigation systems would be useful in small farms and large farms. They are also easily adaptable in nature and may incorporate such functions like weather forecasts and predictive analysis in the future using advanced machines, as well as facilitating management of water distribution in different fields. Ultimately, the Smart Irrigation System represents an evolution towards smart technology [1]. It consists of integration of internet technology and machine learning in conjunction with cloud monitoring for the benefit of farmers [1]. These qualities make the smart irrigation system an important technological tool in helping farmers conserve water, cultivate crops, and manage the earth.

References

[1] S. R. Boralkar, P. R. Thote, and S. A. Kulkarni, “IoT Based Smart Agriculture System Using ESP32,” International Conference on Smart Systems and Inventive Technology (ICSSIT), IEEE, 2024, pp. 112–118. [2] P. R. Thote, S. R. Boralkar, and A. V. Patil, “Machine Learning and IoT Based Smart Irrigation System,” International Conference on Advances in Computing, Communication and Control (ICAC3), IEEE, 2024, pp. 215–220. [3] M. Morchid, D. El Ouadghiri, and H. Ouchra, “Smart Irrigation Using Embedded Systems and Regression-Based Machine Learning Techniques,” IEEE Access, vol. 13, 2025, pp. 14567–14578. [4] A. Kumar and R. Singh, “Crop Recommendation System Using Machine Learning Techniques,” International Journal of Computer Applications, vol. 176, no. 39, 2023, pp. 18–23. [5] H. Patel and M. Shah, “Random Forest Based Crop Prediction Using Soil and Climatic Parameters,” International Journal of Advanced Research in Computer Science, vol. 14, no. 2, 2023, pp. 97–102. [6] J. A. Ramírez, L. F. Gómez, and P. Torres, “IoT-Based Automated Irrigation System Using ESP32 and Cloud Platforms,” Procedia Computer Science, vol. 198, 2024, pp. 456–463. [7] Google, “Firebase Realtime Database Documentation,” Available: https://firebase.google.com/docs/database [8] F. Pedregosa, G. Varoquaux, A. Gramfort et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, 2011, pp. 2825–2830. [9] R. Jain and P. Mehta, “Soil Moisture Prediction Using Linear Regression Model,” International Journal of Engineering Research and Technology (IJERT), vol. 11, no. 6, 2022, pp. 210–214. [10] L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, 2001, pp. 5–32.

Copyright

Copyright © 2026 Sarang Kathilkar, Aditya Chavan, Kanak Sahu, Dhanashree Phalke, Smita Shedbale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.